sjewo

plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,wts.only=F,rel.rsc=5,circle.cex=5,node.labs=T,
  line.stag=NULL,cex.val=1,alpha.val=1,circle.col='lightgrey',pos.col='black',neg.col='grey',...){

  require(scales)
	
  #gets weights for neural network, output is list
  #if rescaled argument is true, weights are returned but rescaled based on abs value
  nnet.vals<-function(mod.in,nid,rel.rsc){

    library(scales)

sjewo

# Author: Joona Lehtomäki <joona.lehtomaki@gmail.com>
# Updated: 13.11.2011
# Version: 0.0.1

if (!require("rgdal")) {
	install.packages("rgdal")
}

if (!require("raster")) {
	install.packages("raster")

sjewo

/*
 * NAME
 * 
 * statistics-distributions.js - JavaScript library for calculating
 *   critical values and upper probabilities of common statistical
 *   distributions
 * 
 * SYNOPSIS
 * 
 * 

sjewo

library(XML)
library(plyr)
library(pbapply)

xlsxToR <- function(file, keep_sheets = NULL, header = FALSE) {
  
  temp_dir <- file.path(tempdir(), "xlsxToRtemp")
  suppressWarnings(dir.create(temp_dir))
  
  file.copy(file, temp_dir)

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#' Simplified loading and installing of packages
#'
#' This is a wrapper to \code{\link{require}} and \code{\link{install.packages}}.
#' Specifically, this will first try to load the package(s) and if not found
#' it will install then load the packages. Additionally, if the 
#' \code{update=TRUE} parameter is specified it will check the currently 
#' installed package version with what is available on CRAN (or mirror) and 
#' install the newer version.
#' 
#' @param pkgs a character vector with the names of the packages to load.

sjewo

# install.packages("rPython")
# requires pandas

stata13 <- function(dat,
                    convert.factors = TRUE){
    
  require("rPython")
  # make dat known to python
  python.assign('dat',dat)
  

sjewo

# somewhat hackish solution to:
# https://twitter.com/EamonCaddigan/status/646759751242620928
# based mostly on copy/pasting from ggplot2 geom_violin source:
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r

library(ggplot2)
library(dplyr)


"%||%" <- function(a, b) {